April 22, 2019

Introduction

  • A natural lake, in the Great Basin in western Nevada in the United States.

  • 11 mi (17 km) long and 5 mi (8 km) wide.

  • Fed from the north by the Walker River and has no natural outlet.

  • Over the years changes have occured on the lake and its environs.

  • There has been reductions in inflow and continued evaporation of water from the lake.

  • These changes were detected using Random Forest and Maximum Likelihood Classification.

Problem Statement

  • The introduction of agriculture upstream of Walker Lake has resulted in the diversion of water from the Walker River and its tributaries for irrigation.

  • These diversions have resulted in a severe drop in the level of the lake.

  • The reduction in the volume of water has resulted in a higher concentration of total dissolved solids.

  • Decline of the lake's fishery which is having a dramatic impact on the species of birds using the lake.

Downloaded Landsat 5 and 8 scene for this study

#Load both Landsat 5 and 8 scenes
LS_1984 <- brick("Walker_Lake/1984/1984_Composite/Tif/1984.tif")
LS_2018 <- brick("Walker_Lake/2018/2018_Composite/Tif/2018.tif")
#Load the study area boundary shapefile
Study_area <- readOGR("Walker_Lake/Study_Area_Boundary/Boundary.shp")

Reprojecting coordinate system

  • The coordinate system of Landsat scenes was WGS_1984_UTM_Zone_11N.

  • The same was used when creating the study area boundary shapefile in ArcMap.

projection(LS_1984)
projection(LS_2018)
projection(Study_area)
Study_area <- spTransform(Study_area, CRS(proj4string(LS_1984)))
#Set NA value to RASTER NA value.
NAvalue(LS_1984) <- 0
NAvalue(LS_2018) <- 0

Plotting Study Area

#Plot the study area on top of the Landsat image to see if the reprojection has worked using the plotRGB tool
plotRGB(LS_1984, r=1, g=4, b=7, stretch="lin")
plotRGB(LS_2018, r=5, g=6, b=4, stretch='lin')
plot(Study_area, col="green", add=TRUE)

#Resize Landsat scenes to the required study area using the crop function
StudyArea_1984 <- crop(LS_1984, Study_area)
StudyArea_2018 <- crop(LS_2018, Study_area)
plotRGB(StudyArea_1984,r=1, g=4, b=7, stretch="lin")
plotRGB(StudyArea_2018,r=5, g=6, b=4, stretch="lin")

Walker Lake 1984

Walker Lake 2018

Study Area

img <- stack(StudyArea_1984, StudyArea_2018)
plotRGB(img,r=1, g=4, b=7, stretch="lin")
td <- shapefile("Walker_Lake/Training_Data/Training_Data.shp")
plot(td, col="yellow", add=T)

Image Classification using RF and MLC

#Image classification of the two landsat scenes
beginCluster()
set.seed(7)
img1984_2018_rf = 
  superClass(img = img, trainData = td, trainPartition = 0.7, 
              responseCol = "Class_Name", nSamples = 500,
              areaWeightedSampling = TRUE,  
              model = "rf", mode = "classification", verbose = FALSE)

## Image Classification using MLC
set.seed(7)
img1984_2018_mlc = 
  superClass(img = img, trainData = td, trainPartition = 0.7, 
              responseCol = "Class_Name", nSamples = 500, 
              areaWeightedSampling = TRUE,  
              model = "mlc", mode = "classification", verbose = FALSE)
endCluster()

Plotting Classification Output of RF

#Random Forest
windows()

ggR(img1984_2018_rf$map, layer = 1, geom_raster = TRUE,forceCat=TRUE)+
  theme(legend.title = element_text(size = 12, face = "bold"))+
  theme(legend.text = element_text(size = 10))+
  scale_fill_manual(values = c("lightsalmon4", "chocolate1", "darkgreen", 
                    "burlywood3", "chartreuse", "blue"),
                    labels=img1984_2018_rf$classMapping$class,
                    name="class name:\n",
                    na.value = "NA",na.translate=FALSE
  )+
  xlab("")+
  ylab("")
north2(img1984_2018_rf$map, x = 0.1, y = 0.8, scale = 0.09, symbol = 1)

RF Output

  • Overall accuracy attained was 98.9%

  • kappa value of 0.986

Plotting Classification Output of MLC

windows()

ggR(img1984_2018_mlc$map, layer = 1, geom_raster = TRUE,forceCat=TRUE)+
  theme(legend.title = element_text(size = 12, face = "bold"))+
  theme(legend.text = element_text(size = 10))+
  scale_fill_manual(values = c("lightsalmon4", "chocolate1", "darkgreen", 
                    "burlywood3", "chartreuse", "blue"),
                    labels=img1984_2018_mlc$classMapping$class,
                    name="class name:\n",
                    na.value = "NA",na.translate=FALSE
  )+
  xlab("")+
  ylab("")
north2(img1984_2018_mlc$map, x = 0.1, y = 0.785, scale = 0.09, symbol = 1)

MLC Output

  • Overall accuracy attained was 95%

  • kappa value of 0.926

Conclusion

  • RF attained the highest accuracy and Kappa value.

  • Water, land and vegetation are very easy classes to identify.

  • This can be attributed to the high accuracies in general.

  • RF can be said to be the best classifer in this case.

  • Put in place measures to prevent Lake Walker's extinction.

Reference

  • Beutel, M. W., Horne, A. J., Roth, J. C., & Barratt, N. J. (2001). Limnological effects of anthropogenic desiccation of a large, saline lake, Walker Lake, Nevada. In Saline Lakes (pp. 91-105). Springer, Dordrecht.

  • Dickerson, B. R., & Vinyard, G. L. (1999). Effects of high levels of total dissolved solids in Walker Lake, Nevada, on survival and growth of Lahontan cutthroat trout. Transactions of the American Fisheries Society, 128(3), 507-515.

  • To access the script: https://github.com/ChristabelAnsah